96 lines
2.8 KiB
Python
96 lines
2.8 KiB
Python
import os
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import torch
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import torch.nn as nn
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import numpy as np
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import random
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from PIL import Image
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from tqdm import tqdm
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from safetensors.torch import save_file
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from interposer import Interposer
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from vae import get_vae
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# options
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target_dev = "cuda"
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target_steps = 500000
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save_every_n = 50000
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latent_src = "v1"
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latent_dst = "xl"
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class Latent:
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def __init__(self, md5, lat_src, lat_dst, dev):
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if lat_src == "v1": src = os.path.join("latent_v1", f"{md5}.npy")
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if lat_src == "v2": src = os.path.join("latent_v2", f"{md5}.npy")
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if lat_src == "xl": src = os.path.join("latent_xl", f"{md5}.npy")
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if lat_dst == "v1": dst = os.path.join("latent_v1", f"{md5}.npy")
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if lat_dst == "v2": dst = os.path.join("latent_v2", f"{md5}.npy")
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if lat_dst == "xl": dst = os.path.join("latent_xl", f"{md5}.npy")
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self.src = torch.from_numpy(np.load(src)).to(dev)
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self.dst = torch.from_numpy(np.load(dst)).to(dev)
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vae = None
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def sample_decode(latent, filename, version):
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global vae
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if not vae:
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vae = get_vae(version, fp16=True)
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vae.to("cuda")
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latent = latent.half().to("cuda")
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out = vae.decode(latent).sample
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out = out.cpu().detach().numpy()
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out = np.squeeze(out, 0)
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out = out.transpose((1, 2, 0))
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out = np.clip(out, -1.0, 1.0)
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out = (out+1)/2 * 255
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out = out.astype(np.uint8)
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out = Image.fromarray(out)
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out.save(filename)
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if not os.path.isdir("models"): os.mkdir("models")
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if __name__ == "__main__":
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print("Loading latents from disk")
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latents = []
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for i in tqdm(os.listdir("images")):
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md5 = os.path.splitext(i)[0]
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latents.append(Latent(md5, latent_src, latent_dst, target_dev))
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if os.path.isfile(f"test_{latent_src}.npy"):
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sample_latent = torch.from_numpy(np.load(f"test_{latent_src}.npy")).to(target_dev)
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else:
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sample_latent = random.choice(latents).src
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model = Interposer()
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model.to(target_dev)
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criterion = torch.nn.MSELoss(size_average=False)
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optimizer = torch.optim.SGD(model.parameters(), lr=1e-8)
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for t in tqdm(range(target_steps)):
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# io = latents[t%len(latents)]
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io = random.choice(latents)
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y_pred = model(io.src) # forward
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loss = criterion(y_pred, io.dst) # loss
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# backward
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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# print loss
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if t%1000 == 0: tqdm.write(f"{t} - {loss.data.item():.2f}")
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# sample/save
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if t%save_every_n == 0:
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out = model(sample_latent)
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output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{t/1000}k"
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sample_decode(out, f"{output_name}.png", latent_dst)
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save_file(model.state_dict(), f"{output_name}.safetensors")
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# save final output
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output_name = f"./models/{latent_src}-to-{latent_dst}_interposer_e{target_steps/1000}k"
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sample_decode(out, f"{output_name}.png", "v1")
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save_file(model.state_dict(), f"{output_name}.safetensors")
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